These are my installation-tricks and notes for running Linux on a 2021 Thinkpad P14s Gen2 with AMD Ryzen 7 5850U. It should also be suitable for the Thinkpad T14 Gen2 AMD as they are technically the same modell.
Meanwhile there is also a good test on youtube and an entry in the arch-wiki, which also comments some points mentioned here.

• The API key wasn't in the shell environment. It lives in ~/.Renviron, so the scripts fall back to reading GOOGLE_API_KEY from there.
• Find model IDs by listing them, not by guessing marketing names: GET https://generativelanguage.googleapis.com/v1beta/models?pageSize=200 (the displayName field maps the two). The ones used:

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

An unofficial workaround until CD PROJEKT RED or Valve fix this properly. As soon as the 5.0 executable sees Wine, it turns off Streamline (DLSS SR/RR, DLSS-G, Reflex), ray tracing, path tracing and FSR frame generation. Everything here brings them back.

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Rodar o Claude Code em uma VM Debian 13 ("isolated") dentro do host (Debian + Incus) de forma que:

• A VM tenha Internet (API da Anthropic, APT, Docker Hub, PyPI, GitHub para clonar), mas não alcance o host nem outras máquinas na rede local
• A VM não tenha nenhuma credencial do host: sem chave SSH privada, token do GitHub, gh, .netrc, ~/.docker/config.json. Clonar repositório público funciona; push (e clone de privado) não, porque não há com o que autenticar

The Grok Bot host builds model requests, runs tools and saves the agent's state. Hosted services also handle identity, scheduling, sync and other features.

A message starts in the desktop app. The desktop sends it to the box host and shows the response. It also manages account connections and can run tools on the user's computer. The host builds the model's context, calls the provider, runs requested tools and saves the results. It then decides whether another model call is needed. The visible chat, model conversation and learned memory use separate stores. Changing the model provider leaves the other hosted service calls in place.

The original architecture sections cover public host 17e335e and desktop 0.52. The later comparison adds five-host source analysis, desktop .57.1 findings and isolated runtime measurements. Host and desktop have separate version number